Basic usage

A simple example demonstrating how to use mass-composition.

Design notes: Once data is loaded chemical analyte names and H2O will conform to the internal standard.

import pandas as pd

from elphick.mass_composition import MassComposition
from elphick.mass_composition.datasets.sample_data import sample_data

Create a MassComposition object

We get some demo data in the form of a pandas DataFrame

df_data: pd.DataFrame = sample_data()
df_data

	wet_mass	mass_dry	FE	SIO2	al2o3	LOI	group
index
0	100.0	90.0	57.0	5.2	3.0	5.0	grp_1
1	90.0	80.0	59.0	3.1	1.7	4.0	grp_1
2	110.0	90.0	61.0	2.2	0.9	3.0	grp_2

Construct a MassComposition object

obj_mc: MassComposition = MassComposition(df_data)
print(obj_mc)

unnamed
<xarray.Dataset> Size: 216B
Dimensions:   (index: 3)
Coordinates:
  * index     (index) int64 24B 0 1 2
Data variables:
    mass_wet  (index) float64 24B 100.0 90.0 110.0
    mass_dry  (index) float64 24B 90.0 80.0 90.0
    H2O       (index) float64 24B 10.0 11.11 18.18
    Fe        (index) float64 24B 57.0 59.0 61.0
    SiO2      (index) float64 24B 5.2 3.1 2.2
    Al2O3     (index) float64 24B 3.0 1.7 0.9
    LOI       (index) float64 24B 5.0 4.0 3.0
    group     (index) object 24B 'grp_1' 'grp_1' 'grp_2'
Attributes:
    mc_name:            unnamed
    mc_vars_mass:       ['mass_wet', 'mass_dry']
    mc_vars_chem:       ['Fe', 'SiO2', 'Al2O3', 'LOI']
    mc_vars_attrs:      ['group']
    mc_interval_edges:  {}

Demonstrate the aggregate method

i.e. weight average of the dataset, a.k.a. head grade

obj_mc.aggregate()

	mass_wet	mass_dry	H2O	Fe	SiO2	Al2O3	LOI
name
unnamed	300.0	260.0	13.333333	59.0	3.515385	1.873077	4.0

obj_mc.aggregate(as_dataframe=False)

<xarray.Dataset> Size: 56B
Dimensions:   (index: 1)
Dimensions without coordinates: index
Data variables:
    mass_wet  (index) float64 8B 300.0
    mass_dry  (index) float64 8B 260.0
    H2O       (index) float64 8B 13.33
    Fe        (index) float64 8B 59.0
    SiO2      (index) float64 8B 3.515
    Al2O3     (index) float64 8B 1.873
    LOI       (index) float64 8B 4.0
Attributes:
    mc_name:            Aggregate of unnamed
    mc_vars_mass:       ['mass_wet', 'mass_dry']
    mc_vars_chem:       ['Fe', 'SiO2', 'Al2O3', 'LOI']
    mc_vars_attrs:      []
    mc_interval_edges:  {}

xarray.Dataset

• Dimensions:
  • index: 1
• Coordinates: (0)
• Data variables: (7)
  • mass_wet
    (index)
    float64
    300.0

    units :

    mass_units

    standard_name :

    Wet Mass

    mc_type :

    mass

    mc_col_orig :

    wet_mass

    array([300.])

  • mass_dry
    (index)
    float64
    260.0

    units :

    mass_units

    standard_name :

    Dry Mass

    mc_type :

    mass

    mc_col_orig :

    mass_dry

    array([260.])

  • H2O
    (index)
    float64
    13.33

    units :

    %

    standard_name :

    H2O

    mc_type :

    moisture

    mc_col_orig :

    H2O

    array([13.33333333])

  • Fe
    (index)
    float64
    59.0

    units :

    %

    standard_name :

    Fe

    mc_type :

    chemistry

    mc_col_orig :

    FE

    array([59.])

  • SiO2
    (index)
    float64
    3.515

    units :

    %

    standard_name :

    SiO2

    mc_type :

    chemistry

    mc_col_orig :

    SIO2

    array([3.51538462])

  • Al2O3
    (index)
    float64
    1.873

    units :

    %

    standard_name :

    Al2O3

    mc_type :

    chemistry

    mc_col_orig :

    al2o3

    array([1.87307692])

  • LOI
    (index)
    float64
    4.0

    units :

    %

    standard_name :

    LOI

    mc_type :

    chemistry

    mc_col_orig :

    LOI

    array([4.])

• Indexes: (0)
• Attributes: (5)

  mc_name :

  Aggregate of unnamed

  mc_vars_mass :

  ['mass_wet', 'mass_dry']

  mc_vars_chem :

  ['Fe', 'SiO2', 'Al2O3', 'LOI']

  mc_vars_attrs :

  []

  mc_interval_edges :

  {}

Aggregate by a group variable

obj_mc.aggregate(group_var='group')

	mass_wet	mass_dry	H2O	Fe	SiO2	Al2O3	LOI
group
grp_1	190.0	170.0	10.526316	57.941176	4.211765	2.388235	4.529412
grp_2	110.0	90.0	18.181818	61.000000	2.200000	0.900000	3.000000

Filter the object

Filter with a criteria, just like pandas

obj_mc.query(queries={'index': 'Fe>58'}).aggregate()

	mass_wet	mass_dry	H2O	Fe	SiO2	Al2O3	LOI
name
unnamed	200.0	170.0	15.0	60.058824	2.623529	1.276471	3.470588